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Soft Fault Classification of Analog Circuits Using Network Parameters and Neural Networks

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Abstract

A new method to identify component faults in analog circuits is proposed using network parameters like driving point impedance, transfer impedance, voltage gain and current gain. Using Monte-Carlo simulation each component of the circuit is varied within its tolerance limit and samples of each network parameter are found for fault free circuit. Similarly all possible single faults are introduced and the corresponding samples of network parameters are found. Fault classification is done through neural network. The proposed method is validated through second order Sallenkey band pass filter. Numerical results are presented to clarify the proposed method and prove its efficiency.

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Authors and Affiliations

  1. Department of Electrical & Electronics Engineering, Coimbatore Institute of Technology, 641014, Coimbatore, India

    A. Kavithamani & V. Manikandan

  2. Department of Electrical and Electronics Engineering, Government College of Technology, 641013, Coimbatore, India

    N. Devarajan

Authors
  1. A. Kavithamani
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  2. V. Manikandan
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  3. N. Devarajan
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Corresponding author

Correspondence to A. Kavithamani.

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Responsible Editor: K. K. Saluja

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Kavithamani, A., Manikandan, V. & Devarajan, N. Soft Fault Classification of Analog Circuits Using Network Parameters and Neural Networks. J Electron Test 29, 237–240 (2013). https://doi.org/10.1007/s10836-013-5370-3

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  • DOI: https://doi.org/10.1007/s10836-013-5370-3

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